SwISS: a scalable Markov chain Monte Carlo divide-and-conquer strategy

DOI10.1002/sta4.523MaRDI QIDQ6548764

Chris Sherlock, Christopher Nemeth, Callum Vyner

Publication date: 3 June 2024

Published in: Stat (Search for Journal in Brave)

zbMATH Keywords

Markov chain Monte Carlobig datadivide-and-conquerparallel MCMC

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Emerging directions in Bayesian computation

Cites Work

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• Asymptotics in statistics. Some basic concepts.
• Merging MCMC subposteriors through Gaussian-process approximations
• Nonparametric density estimation with a parametric start
• Control variates for stochastic gradient MCMC
• Speeding up MCMC by Delayed Acceptance and Data Subsampling
• Likelihood inflating sampling algorithm
• Stochastic Gradient Markov Chain Monte Carlo

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